Imagine a co-pilot for the people running our electricity and train networks. Instead of a human doing everything by hand or a robot taking over completely, this system suggests the best moves based on real-time data. It's like having a smart GPS that not only shows the way but explains why it chose that route to keep things safe.
Operators of critical infrastructure face increasing uncertainty and complexity that exceed human cognitive limits, yet full AI automation is too risky for safety-critical systems.
A hypervision tool for AI recommendations and a set of reinforcement learning agents validated in 6 industry use cases.
If you are a power grid operator dealing with unpredictable energy demand and weather disruptions — this project developed AI agents that predict failures and suggest robust actions. This helps maintain electricity stability while keeping a human in the loop for final approval.
If you are a railway manager dealing with complex train scheduling and network delays — this project developed reinforcement learning tools that optimize traffic flow. It reduces the mental load on dispatchers by providing transparent, explainable recommendations.
If you are an air traffic controller dealing with high-density airspace and safety risks — this project developed a system to augment human cognition. It uses digital simulations to validate AI decisions before they are applied to real flights.
Based on available project data, no specific pricing or cost structures are provided as this is a research-funded project.
The project uses hierarchical and distributed AI agents specifically designed to enhance scalability for power grids and railway domains.
Based on available project data, the project promotes community engagement through open-source releases, though specific commercial licensing terms are not listed.
The project developed interoperability connectors that link the AI logic to operational agents for real-time exchange of recommendations and KPIs.
The project period runs from 2023-10-01 to 2027-03-31, indicating it is currently in the development and validation phase.
The consortium is well-balanced for technology transfer, featuring 17 partners with a 35% industry ratio (6 companies). The presence of 2 SMEs and 7 universities across 8 countries suggests a strong mix of academic research and practical industrial application, specifically targeting the energy and transport sectors.
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